Geology topics

Andy Raedeke

Publications and source records attributed to Andy Raedeke.

3 recordsLinked to original sources

USGS2021

SUAS and machine learning integration in waterfowl population surveys

The rapid technological development of small Unmanned Aircraft Systems (sUAS) has led to an increase in capabilities of aerial image collection and analysis for monitoring a variety of wildlife species including waterfowl. Biologists mainly rely on conducting ocular surveys from fixed-wing aircraft or helicopters to estimate waterfowl abundance. sUAS provide an alternative that is safer, less expensive, and more flexible. Researchers have attempted to estimate waterfowl abundance from aerial imagery, but this method has proven to be too time consuming. Machine learning provides the opportunity to more efficiently estimate waterfowl abundance from aerial imagery. In this paper, we present a new integrated system of sUAS and machine learning for waterfowl population surveys. This system provides a user-friendly process for sUAS survey design, deployment, and data post-processing using deep learning methods to automatically detect and count waterfowl. To develop this system, we conducted many sUAS flights to capture a diversity of imagery and assembled six datasets of imagery taken from both fix-winged aircraft and sUAS flights. We used these datasets to develop and evaluate state-of-the-art deep learning models for waterfowl detection. Our system of using a combination of sUAS and machine learning has proved to be an efficient and accurate approach for collecting, analyzing, and estimating waterfowl abundance.

Conference Paper↗

USGS2019

Evaluating tradeoffs in the response of Sora (Porzana carolina) and waterfowl to the timing of early autumn wetland inundation

Wetland loss has increased the importance of multi-species management in remaining wetlands, which provide habitat for a multitude of wetland-dependent species. Many public wetlands across the mid-latitude United States are managed as moist soil impoundments with emphasis on migratory waterfowl. However, how the timing of these water management decisions affects rails is still uncertain. Wetland managers identified this as an area of uncertainty regarding timing of alternative water management strategies to benefit waterfowl and rails, which was addressed through a 3-year management experiment. Sora ( Porzana carolina ) and waterfowl were surveyed on 10 public wetland properties in Missouri, USA from 2014-2016, and their responses to early autumn inundation of managed palustrine wetlands were compared. A total of 558 Sora surveys detected 5,755 birds (20.6 birds/survey ± 30.8 SD), and 1,304 waterfowl surveys detected 1,411,779 birds (15,686.4 birds/survey ± 23,933.9 SD). Sora responded positively (birds/ha) to inundation of moist soil impoundments earlier in autumn migration (August). The top model for Sora included treatment, year and region of Missouri. There was no difference in waterfowl abundance between early or late inundation. Inundating wetlands earlier in autumn migration can provide habitat for migrating Sora without negative effects on waterfowl use of those wetlands, and wetland managers can incorporate this into their decision-making framework.

Missouri↗

USGS2018

A review of Bayesian belief network models as decision-support tools for wetland conservation: Are water birds potential umbrella taxa?

Creative approaches to identifying umbrella species hold promise for devising effective surrogates of ecological communities or ecosystems. However, mechanistic niche models that predict range or habitat overlap among species may yet lack development. We reviewed literature on taxon-centered Bayesian belief network (BBN) models to explore a novel approach to identify umbrella taxa identifying taxonomic groups that share the largest proportion of habitat requirements (i.e., states of important habitat variables) with other wetland-dependent taxa. We reviewed and compiled published literature to provide a comprehensive and reproducible account of the current understanding of habitat requirements for freshwater, wetland-dependent taxa using BBNs. We found that wetland birds had the highest degree of shared habitat requirements with other taxa, and consequently may be suitable umbrella taxa in freshwater wetlands. Comparing habitat requirements using a BBN approach to build species distribution models, this review also identified taxa that may not benefit from conservation actions targeted at umbrella taxa by identifying taxa with unique habitat requirements not shared with umbrellas. Using a standard node set that accurately and comprehensively represents the ecosystem in question, BBNs could be designed to improve identification of umbrella taxa. In wetlands, expert knowledge about hydrology, geomorphology and soils could add important information regarding physical landscape characteristics relevant to species. Thus, a systems-oriented framework may improve overarching inferences from BBNs and subsequent utility to conservation planning and management.

Biological Conservation↗